Recommender systems have become the dominant means of curating cultural content, significantly influencing individual cultural experience. Since recommender systems tend to optimize for personalized user experience, they can overlook impacts on cultural experience in the aggregate. After demonstrating that existing metrics do not center culture, we introduce a new metric, commonality, that measures the degree to which recommendations familiarize a given user population with specified categories of cultural content. We developed commonality through an interdisciplinary dialogue between researchers in computer science and the social sciences and humanities. With reference to principles underpinning public service media systems in democratic societies, we identify universality of address and content diversity in the service of strengthening cultural citizenship as particularly relevant goals for recommender systems delivering cultural content. We develop commonality as a measure of recommender system alignment with the promotion of content toward a shared cultural experience across a population of users. We empirically compare the performance of recommendation algorithms using commonality with existing metrics, demonstrating that commonality captures a novel property of system behavior complementary to existing metrics. Alongside existing fairness and diversity metrics, commonality contributes to a growing body of scholarship developing `public good' rationales for machine learning systems.

翻译：推荐系统已成为策展文化内容的主要手段，深刻影响着个体的文化体验。由于推荐系统倾向于优化个性化用户体验，它们可能忽视对整体文化体验的影响。在证明现有指标未能聚焦文化层面后，我们引入了一个新指标——"共性"（commonality），用于衡量推荐系统使特定用户群体熟悉指定类别文化内容的程度。我们通过与计算机科学、社会科学与人文学科研究者的跨学科对话，开发了"共性"这一指标。参照民主社会中公共服务媒体体系原则，我们指出地址普适性和内容多样性——服务于增强文化公民意识——是推荐系统在传递文化内容时尤为相关的目标。我们将"共性"发展为衡量推荐系统在促进用户群体共享文化体验方面的对齐程度指标。我们通过实验对比了基于"共性"的推荐算法性能与现有指标，证明"共性"捕捉到了系统行为中与现有指标互补的新特性。与现有的公平性、多样性指标一起，"共性"为日益增长的、基于"公共利益"视角论证机器学习系统的学术研究做出了贡献。